{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy.random import randn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x8ad8f98>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3N2/ualk5kTQoa1KvqamhqampnJeUFOli4WeR1FD3i4hIgiipS3m8+y688ELcUYgknpK6lMdFF8FnPwubN8cdiUiidZvUsxTOv8TM/mJmT0U/k0obplS9M84Ij0uXxhuHSMLl0lK/kc6F8yEUbh8b/aRuCrj00KGHwpAhsGRJ3JGIJFq3ST1L4XyR3H3wAZxyCuyyS2ipt7Z2/xoRyUshfeozo/VEb6jUdf+kQrz8MtxxB3zpS7B1Kzz2WO6vbW2F5ubSxSaSMPkm9V8Bowhr6W0AfpHtQDNrMLMmM2tqaWnJ83JS1Z57Ljyedx4sXgxf/GLurz3/fKithbVrSxObSMLkldTdfaO7f+juO4DrCOsxZjt2rrvXunvt4MGa7FeV1qyBr3wF3nwzv9evXg1moaVeXw+7757b6/78Z7j2Wqirg/33z+/aIimTV1KPVhBp8zVgVbZjJQE+8YnQZXLddfm9/rnnoKYG+vWDDRvg0kth/fruX/cv/xJeM28euMNf/5rf9UVSJJchjQuBx4ADzGy9mU0D/p+ZPWtmzwBHAd8tcZwSpwMOgL/9W5gzJ7+bnL17wxFHhN/fegt+9CO4++6uX/Pgg6Ef/qKLYNgwmDABvvWtnl9bJGVyGf0yxd2Huntvdx/u7vPc/Ux3P9jdD3H3+rbVuiWBnnsODjwQDjsMXn0VGvNYke03v4Hf/jb8fsABMGoU/PrXcOutsCnLkpF/+hOMGQPf+17ouvnc5+Dmm3Nr4YukmGaUStceeACefx6mTw9dKNdcU9j5zODCC0PSPvXUcM733+983Nlnw9NPQ9++Yfu734UdOwq/vkjCKalL1x54ILSsR42CmTNh2zZ4553cX79kSbhBuq5dAcVp08JN18cfD/30ffrsfO7ZZ0O3C4Rx7W1qasLomc98pqC3I5J0ZV2jtLa21lV6t4p88AHstRd84xvwq1+F/vRevUJrO1eXXQY//GH4Y5Bt1Is7XH89HHwwfPvb8Je/hLHte+zRo3DNrNnda3v0IpGEKWs9dakyTzwRWuXHHBO221rOr70G//3f8M1vdn+OtpEvXQ1j3LYtjIjZuDF0xdx5Z48TuogE6n6R7Pr1C630o476+P4rroAzzww3O7uzenW4ydmVPfYIN00hnPfEE/OLV0TUUpcO1q8PXS59+8LYsfBf/9X5mKuuCiNhvvWt0B1z9tmZz9XaGm6yHnts99c9/HB45RXYe++CwhdJO7XUZaf168MN0b32gokT4aabQn93R7vuCrfdBpMmwTnnZB9z/s47MHkyHHlkbtcfOjT8kRCRvCmpy06/+U3o0z7jjFBEa+pU+MMfMh+7666waFGo43L55ZmT/4ABIfmfcEJp4xaRjyipp9Wjj8L9939835FHhtme118fJgVt2BC6RbLZffcwZHHp0jAipv1s05tughUrShO7iGSlIY1p1Noapu5DGMkyfHjh53SHv/kbOOQQGDcutPInTuy+HEARaUijiFrq6XTnneFxn31CNwqE6f+FLAz917+GErk33hhGzBxwQOabrCJSUmqpp9Hf/V2Y4PPSS+HG5Hvvwb77hlEqbTVa8vXqqyGx/+M/wn77FSXcXKmlLqKWevqsXw9NTWHKf69e4Ybo8cfDli3w939f+PlHjAj98mVO6CISaJx62gwfHhJ7W7fLU0+F+i5Dh4bytiJS1ZTU06S1NUz132uvnfv+4R/grrvg6KM1RlwkAZTU0+KZZ0KXywcfhAUo2kra9upV1hEqIlJa6lNPuq1bw2iUsWNDYj/jjJ0JXUQSRy31pPvxj+GWW2DWLPj+98MsTxFJLCX1JHOHNWtCS/2nP407GhEpg26TupndABwPbHL3g6J9A4FbgBrgFeBUd3+zdGFKXszgvvvCxCARSYVc+tRvBCZ22DcLWOHuo4EV0bZUkrffDrVbAHbbLd5YRKRsuk3q7v4IsKXD7hOABdHvCwCtalBprr0W9t9/Z2IXkVTId/TLEHffABA9amWDSrJtG1x9dViGbujQuKMRkTIq+ZBGM2swsyYza2ppaSn15QRCK33LFvi3f4s7EhEps3yT+kYzGwoQPW7KdqC7z3X3WnevHTx4cJ6Xk5xt3QpXXhnquXRVC11EEinfpN4ITI1+nwosLk44UrAVK8IycpdeGnckIhKDXIY0LgTGAYPMbD1wMXAFsMjMpgGvAqeUMkjpgZNOgnXrYNiwuCMRkRh0m9TdfUqWp44ucixSqI0bYcgQJXSRFFPtl6R44QUYORLmzIk7EhGJkZJ6ErS2hhK6u+0GX/963NGISIxU+yUJfv5zePzxsBSdxqWLpJpa6tVu1Sq4+GI4+WQ4/fS4oxGRmCmpV7sXXwxL1P3yl6GAl4ikmpJ6tXEPC0dfc03Y/vrX4fnnQRO7RAQl9erx+utw7rnw6U/Dl74EP/oRbIom8vbuHW9sIlIxlNSrQWtr6DO/7jr44hfhhhtg7VrYW3XUROTjNPqlGmzdGvrL58+HKdnmgomIKKlXh0GD4JFH4BP6h5WIdE1JvdK4h26Wt9+G3XcPo1suvFBdLSKSEyX1StPcDNOn79zu3RuOPRYmdlxRUESkMyX1StPcHB5ffBH22AP69IGBA+ONSUSqhpJ6pZk+HerrNd1fRPKiO2+VSAldRPKkpF5JVq0K49FffDHuSESkSimpV5IHHoDbbw+jXkRE8qCkXkkefhhGjdLKRSKSNyX1SrFjB/z+9/CVr8QdiYhUsYJGv5jZK8A7wIdAq7vXFiOoVFq1CrZsUVIXkYIUY0jjUe6+uQjnSY9TT4VevaCuDiZNCotFv/UWHHqokrqIFETdL+Xy2mvw4YehDED//qH//KyzwgIXp50WJhk1NYXSuiIieSo0qTuwzMyazayhGAEl0tNPwyGHwE9+Eqotzp0b6qM/9VSokb58eehPFxEpkLl7/i8229fdXzezvYHlwLnu/kiHYxqABoARI0Ycum7dukLirU7Tp4dFoZ95BkaO7Pz8u++GG6UaylgQM2vWfR1Ju4Ja6u7+evS4CbgTOCzDMXPdvdbdawenccm1HTtg8eLQd54poQP07auELiJFkXdSN7PdzWyPtt+BOmBVsQJLjJUrYeNGOPHEuCMRkRQoZPTLEOBOCyvY7wL81t2XFiWqJLnnnlA+d9KkuCMRkRTIO6m7+1rg80WMJZkuuQROOgn23DPuSEQkBTSksRh27Ah1Wy65pPNzvXuHxaJFRMpA9dSzaWmBXG7susNRR4U1RAcMCEMUe/eGJ5+El18OPz/9qdYXFZGyUKbJ5IknYJ99YMmS7o+9//6Q0C+9NIw932svOP98OO44uOwyeOghJXQRKRtlm0x+/vPQpXL77d0f+4tfwL77wg9+ALvtFvZdfnlY6GLdOo16EZGyUlLv6O23YenSUJtl6dLQvZLN//4vvPFG6HLp02fn/kGDwqiXk06CqVNLH7OISER96h196lPwyivwP/8TarXs2BESfCa77x4Wim5t7fzcmDFw220lDVVEpCMl9fY+/DD0fw8YAMcf3/Wxb74Z6rj07x9ujIqIVAB1v7Q3ezbU1oYyuACrV8P8+Z2Pe/VVOOecUFFx69byxigi0gUl9TYffgjXXhvqsLRNFFq4EM4+e2fiXrUqtOBHjoRbb4WGhtBSFxGpEErqbe65B9auhfPO27nvuONCsl++HLZtC4tarFwJF14Yjr3yyvjiFRHJQH3qba65BkaM+PgQxMMPDy3x++6DU06Bq64KddHHjIkvThGRLqilDvDss2GS0IwZsEu7v3O77ALjx4d+9R074PTTldBFpKJVZlJ/4YXyXu8zn4Hrr4d/+qfOz51xRnh85ZWyhiQiko/KSuoPPwz/8R9w4IFhlaCeePrpUK+lvXffhbvugvff7/q1ffvCtGkwcGDn5046CbZsgf3371k8IiIxqJykvmkTHHtsKIS1555w0UW5v7alBQ47LPSBv/Za2Pfee/C1r4WfurrOCd8dNmwIJQGuuabrmaMDBvT8/YiIxKBykvp114UW9QUXhJ8lS3JfjHnBgvDaTZvg6KPD1P3TTgvFthoawoiVc88Nx956Kxx5JPTrF2q2fP/74biw2IeISFWrjNEvH3wAv/wlTJgAn/0s/PM/hzHjs2bBo492n3CXLAmJ+sorQ13z/v1DV8rs2eHm59lnw7Bh4djNm8MfgG9/O3SpjBwJ48aV/C2KiJSDeVfdDkVWW1vrTU1NnZ9YtCi0rBsbYfLksO8//zN0waxcGW5kdqW1NawD2pa4IXSnZPpjkG2/VD0za3b32rjjEIlTZXS/PPhgaDW3X8fzrLNg/fruE7p7GHrYPqFD9sSthC4iCVZQUjeziWb2gpm9bGaz8j7Rr38Njz/+8WqIvXuHUSk7dsD27Zlft3kzjBoF996b96VFRJIk76RuZr2AOcBxwBhgipnlPzMn09Jx778PBx8MF1/c+bnNm8MMzz//OcwEFRGRglrqhwEvu/tad38f+B1wQnHCivTpAwcdBPPm7Wyt33dfWIRi8OCwwtD48eEYEREpKKkPA15rt70+2ldcM2eG2uULF4btCRPCaJarrgoJvrGx6JcUEalWhQxpzHTHsdNQGjNrABoARuTTTfLlL8PnPhem8H/1q2FB6Msv7/l5RERSoJCW+npgv3bbw4HXOx7k7nPdvdbdawdn6jfvjhn88IdhVudzz+UdrIhIGhSS1J8ARpvZSDPrA5wOlKYv5LTTwo3R8eNLcnoRkaTIu/vF3VvNbCZwP9ALuMHdVxctso4+URlD6kVEKllBZQLc/V5Ag8RFRCqEmr8iIglS1tovZtYCrMvy9CBgc9mCKb+kvz+I/z1+2t3zuBsvkhxlTepdMbOmJBdjSvr7g3S8R5FKp+4XEZEEUVIXEUmQSkrqc+MOoMSS/v4gHe9RpKJVTJ+6iIgUrpJa6iIiUqDYk3rRFtqoIGa2n5k9ZGZrzGy1mX0n2j/QzJab2UvR44C4Yy2EmfUysz+a2T3R9kgzWxm9v1ui8hEiUkaxJvWiL7RROVqB8939QOAIYEb0vmYBK9x9NLAi2q5m3wHWtNv+GXB19P7eBKbFEpVIisXdUi/9QhsxcPcN7v5k9Ps7hMQ3jPDeFkSHLQBOjCfCwpnZcOCrwPXRtgHjgduiQ6r6/YlUq7iTenkW2oiRmdUAXwBWAkPcfQOExA/sHV9kBft34AfAjmh7L2Cru7dG24n7LEWqQdxJPaeFNqqVmX0SuB04z93fjjueYjGz44FN7t7cfneGQxPzWYpUi4KqNBZBTgttVCMz601I6De7+x3R7o1mNtTdN5jZUGBTfBEW5Eig3swmAbsBnyK03Pub2S5Raz0xn6VINYm7pV6+hTbKKOpfngescfer2j3VCEyNfp8KLC53bMXg7he6+3B3ryF8Zg+6+zeAh4CTo8Oq9v2JVLNYk3rUomtbaGMNsKikC22Uz5HAmcB4M3sq+pkEXAFMMLOXgAnRdpJcAHzPzF4m9LHPizkekdTRjFIRkQSJu/tFRESKSEldRCRBlNRFRBJESV1EJEGU1EVEEkRJXUQkQZTURUQSREldRCRB/j8g3AXq3FHbsAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg = plt.figure()\n",
    "# add_subplot可以为图像添加一个子图窗口, 2,2,1表示窗口是一个2*2的窗口，现在是第1个\n",
    "ax1 = fg.add_subplot(2,2,1)\n",
    "ax2 = fg.add_subplot(2,2,2)\n",
    "ax3 = fg.add_subplot(2,2,3)\n",
    "\n",
    "# 直方图\n",
    "ax1.hist(randn(100), bins = 20, color = 'k', alpha = 0.3)\n",
    "# 散点图\n",
    "ax2.scatter(np.arange(30), np.arange(30) + 3 * randn(30), s=10, c='k')\n",
    "# 折线图\n",
    "ax3.plot(randn(50).cumsum(), 'r--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在jupyter中如果想再次查看图像，直接调用该图像就可以了\n",
    "plt.show()\n",
    "fg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subplots是创建Figure + add_subplot的快捷方式\n",
    "fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        axes[i,j].hist(randn(500), bins=50, color='k', alpha=0.5)\n",
    "# subplots_adjust用于调整图像间的边距\n",
    "plt.subplots_adjust(wspace=0, hspace=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x541d898>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 折线图\n",
    "# 随机游走\n",
    "randomWalker = randn(30).cumsum()\n",
    "fg = plt.figure()\n",
    "# add_subplot可以为图像添加一个子图窗口, 2,2,1表示窗口是一个2*2的窗口，现在是第1个\n",
    "ax1 = fg.add_subplot(2,2,1)\n",
    "ax2 = fg.add_subplot(2,2,2)\n",
    "ax3 = fg.add_subplot(2,2,3)\n",
    "\n",
    "# 一般表示法\n",
    "ax1.plot(randomWalker, 'ko--')\n",
    "ax2.plot(randomWalker, 'k--')\n",
    "ax3.plot(randomWalker, '--')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 带参数的表示方法 color 颜色, linestyle表示线的类型, marker表示点的标记, 如上面所述的'k--'其中k为颜色表示灰色, --表示序列\n",
    "ax1.plot(randomWalker, linestyle='--', color = 'g', marker = 'o')\n",
    "ax2.plot(randomWalker, linestyle='dashed', color = 'g', marker = 'o')\n",
    "ax3.plot(randomWalker, '--')\n",
    "fg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x54b5e10>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# drawstyle可以表示填充类型, 其中steps-post表示梯度的方式填充\n",
    "plt.plot(randomWalker, linestyle='--', color = 'g', marker = 'o', drawstyle='steps-post')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 10)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# label可以为每一条曲线添加标注\n",
    "plt.plot(randomWalker, linestyle='-', color = 'g', marker = 'o', label = 'default')\n",
    "plt.plot(randomWalker, linestyle='--', color = 'r', marker = 'o', drawstyle='steps-post', label = 'steps-post')\n",
    "# 要加入legend，否则上面的将不生效，legend是生成图例的函数\n",
    "plt.legend(loc='best')\n",
    "# xlim可以限定图标x轴的范围\n",
    "plt.xlim([1, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axis.XTick at 0x8ef5198>,\n",
       " <matplotlib.axis.XTick at 0x8ee3198>,\n",
       " <matplotlib.axis.XTick at 0x8ee3048>,\n",
       " <matplotlib.axis.XTick at 0x8f09400>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "randomWalker = randn(1000).cumsum()\n",
    "fg = plt.figure()\n",
    "ax = fg.add_subplot(1, 1, 1)\n",
    "ax.plot(randomWalker, linestyle='--', color = 'g', marker = 'o', label = 'default')\n",
    "# plt.set_xticks([0, 100, 500, 1000])无法实现，需要是subplot类型\n",
    "# set_xticks可以在图中标记刻度位置\n",
    "ax.set_xticks([0, 100, 500, 1000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set_xticklabels会为输入的xticks依次添加标签, 标签数和xticks数一致，多退少补\n",
    "ax.set_xticklabels(['one', 'tow', 'three'])\n",
    "# 设置图的标题\n",
    "ax.set_title(\"random walker\")\n",
    "fg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 图例设置实践 --金融危机期间的重要日志绘图"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py3",
   "language": "python",
   "name": "py3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
